Real3D-AD: 3D Industrial Anomaly Benchmark

• Real3D-AD is a comprehensive dataset offering ultra-dense 3D point clouds (0.0010–0.0015 mm resolution) for industrial defect detection.
• It simulates real-world inspection with few-shot multi-angle training scans and single-view test scans, enhancing evaluation realism.
• It features manual annotations for incompleteness and redundancy defects alongside benchmark metrics like AUROC for precise performance comparison.

The Real3D-AD dataset is a high-precision, large-scale benchmark designed for point cloud anomaly detection in industrial settings, particularly precision manufacturing. It advances the state of 3D anomaly detection by providing 1,254 real-world 3D objects, each represented by extremely dense point clouds (ranging from tens of thousands up to millions of points per item) with a point resolution of 0.0010 mm–0.0015 mm. Unlike previous RGBD or synthetic datasets, Real3D-AD delivers complete 360° coverage in its prototype scans and annotates anomaly regions with manual ground truth, introducing both incompleteness (e.g., missing data, cross-sectional breaks) and redundancy (e.g., superfluous points) defect types. The dataset’s design, evaluation infrastructure, and accompanying baseline models establish it as a reference point for robust, reproducible research in 3D industrial anomaly detection (Liu et al., 2023).

1. Dataset Composition and Acquisition

Real3D-AD features high-resolution scans of industrial components distributed over 12 object categories. Each object category comprises a “few-shot” training set (up to four prototype scans), each obtained through integrated multi-angle 360° surface acquisition. The test set for each category consists of objects scanned from a single side only, simulating real inspection scenarios where full coverage is unavailable. All point clouds are manually annotated using CloudCompare to delineate normal and anomalous regions, including incompleteness and redundancy defects (Liu et al., 2023).

The dataset’s exceptional point-level resolution (0.0010–0.0015 mm), comprehensive spatial coverage in prototypes, and realistic representation of physical parts clearly distinguish it from other datasets, such as MVTec 3D-AD (0.011 mm resolution, only voxel representation) (Bergmann et al., 2021).

Dataset	Point Resolution	Coverage	Object Type
Real3D-AD	0.0010–0.0015 mm	360° for train; single-view for test	Real industrial part
MVTec 3D-AD	0.011 mm	Single-view	Real product
Eyecandies	0.11 mm	Single-view	Simulated object

2. Annotation and Anomaly Taxonomy

Real3D-AD annotations are created through manual segmentation, highlighting areas of incompleteness (missing elements, cracks, broken boundaries) and redundancy (spurious clusters, excessive point regions). The annotation process emphasizes the heterogeneous nature of industrial defects, ensuring detailed pixel-level region distinction and enabling precise evaluation of detection/localization. Every anomaly is mapped to its spatial domain directly in the 3D point cloud, providing a strict benchmark for evaluating per-point anomaly scores (Liu et al., 2023).

3. Benchmark Protocol and Evaluation Metrics

The dataset is accompanied by ADBench-3D, a benchmark suite standardizing evaluation through established metrics: Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall curve (AUPR), reported at both object-level (whole item) and point-level (anomaly localization). Performance scores are calculated using direct comparisons between predicted and ground-truth anomaly regions, reflecting both detection sensitivity and localization accuracy.

Metric	Purpose	Application
AUROC	Object/Point-level discrimination	All methods
AUPR	Precision-recall balance	All methods

For methods employing region-based metrics, scores such as the per-region overlap (PRO) are deployed in close analogy to the MVTec 3D-AD evaluation protocol:

$$\text{PRO} = \frac{1}{K} \sum_{k=1}^K \frac{|P \cap C_k|}{|C_k|}$$

where $K$ is the count of annotated regions, $P$ the binary anomaly prediction, and $C_k$ the $k$-th ground-truth region.

4. Algorithmic Methodologies

The Real3D-AD dataset motivates novel approaches exploiting high-resolution geometric structure and sparse normal samples.

• Reg3D-AD: A registration-based approach that constructs local and global feature memory banks using few-shot prototype scans. Each incoming test scan is spatially aligned (via RANSAC) to the reference prototype before anomaly scoring. For a test point $m^{test}$, the local anomaly score is computed as:

$s^{l*} = \| m^{test,*} - m^{l*} \|_2$

where $m^{test,*}$ is the farthest point in the test scan (against memory bank), and $m^{l*}$ is its nearest neighbor in the prototype’s local feature space (Liu et al., 2023).

• Recent Advances: State-of-the-art approaches such as MC3D-AD and C3D-AD have adopted unified multi-category learning and continual learning strategies, leveraging geometry-aware masked attention and random feature kernel attention to encode and reconstruct point cloud representations robustly across task streams and classes (Cheng et al., 4 May 2025, Lu et al., 2 Aug 2025).

Ablation studies reveal that increasing the group size for PointMAE-based features improves detection accuracy, whereas expanding the memory bank size yields diminishing returns, suggesting that spatial feature diversity is paramount at extremely high point resolutions (Liu et al., 2023).

5. Experimental Results and Comparative Performance

Real3D-AD’s challenging test suite has elucidated key strengths and limitations of various algorithms:

• Reg3D-AD achieves an average object-level AUROC around 0.70, outperforming PatchCore and M3DM variants on most categories.
• MC3D-AD achieves a 3.1% improvement in AUROC over the best single-category method, yielding object-level AUROC ≈ 0.782 and point-level AUROC ≈ 0.768 (Cheng et al., 4 May 2025).
• R3D-AD, a diffusion-based approach, attains 73.4% image-level AUROC using solely raw point coordinates and robust anomaly suppression via point-level displacement learning (Zhou et al., 15 Jul 2024).
• C3D-AD demonstrates continual learning capabilities, achieving 66.4% AUROC on Real3D-AD, and improving by 14–31% over prior continual baselines (Lu et al., 2 Aug 2025).
• PLANE (Point-Language dual-prompt model) reports a +4.3% Object-level AUROC and +4.1% localization improvement compared to prevailing single-category approaches, underscoring the generalization attained under the multi-class-one-model paradigm (Wang et al., 16 Feb 2025).

Method	Object-level AUROC	Point-level AUROC	Notable Features
Reg3D-AD	~0.70	—	Registration, dual-feature bank
MC3D-AD	~0.782	~0.768	Unified, geometry-aware
R3D-AD	0.734	—	Diffusion, Patch-Gen
C3D-AD	0.664	—	Continual, kernel attention
PLANE	(+4.3%)	(+4.1%)	Dual-prompt, multi-modal

6. Applications, Implications, and Impact

Real3D-AD is specifically calibrated for real industrial defect detection, providing benchmark conditions that reflect practical limitations: limited training samples, restricted viewpoints at test time, and minute anomaly sizes. Its high spatial precision and annotation rigor make it suitable for developing defect detection algorithms in machining, fabrication, and parts inspection. Secondary applications extend to autonomous robotics and quality control, with further hypothetical extensions to medical 3D anomaly detection suggested by its structural richness (Liu et al., 2023).

The dataset’s few-shot prototype regime enforces robustness and generalizability in learning algorithms, stimulating research toward multi-class/object-agnostic detection and continual learning frameworks that can perform across sparse, shifting categories.

7. Future Directions and Technical Considerations

Ongoing research focuses on reducing model reliance on fixed memory banks (e.g., R3D-AD’s diffusion-based reconstruction), improving generalization to unseen categories through multi-modal or prompt-based tuning (as in PLANE), and leveraging geometry-aware attention modules (as in MC3D-AD) for more discriminative spatial feature encoding.

Scaling considerations include the need for efficient handling of extremely large-scale point clouds, diverse object geometries, and the integration of annotation protocols for more subtle or compound defect types. The release of benchmark code and dataset resources ensures reproducibility and facilitates systematic evaluation of future methods.

This collection of findings and methodologies positions Real3D-AD as an essential resource for evaluating and developing high-precision, domain-adaptive, and scalable algorithms in the field of 3D industrial anomaly detection, setting new standards for depth, rigor, and realism in benchmark design (Liu et al., 2023, Cheng et al., 4 May 2025, Zhou et al., 15 Jul 2024, Wang et al., 16 Feb 2025, Lu et al., 2 Aug 2025).